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Multiple regression - Term Paper Example

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Executive Summary Multiple regression is an effective technique to identify a relationship between one dependent variable and multiple independent variables. It is hypothesized the property crime rates per thousand inhabitants is dependent on multiple factors such as per capita income, school dropout percentage, population density, percentage of people living in urban area and so on…
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Multiple regression
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Download file to see previous pages Multiple regression is an effective technique to identify a relationship between one dependent variable and multiple independent variables. It is hypothesized the property crime rates per thousand inhabitants is dependent on multiple factors such as per capita income, school dropout percentage, population density, percentage of people living in urban area and so on. In order to establish a certain relationship between the variables, multiple regression was used. While crimes is the dependent variable, other variables such as state, per capita income, dropouts, average precipitation, public aid recipients, population density, unemployment, percentage of people living in urban areas were considered as independent variable. Minitab was used to perform multiple regression analysis. It was found that only two variables had a significant relationship with the variable crime rates: dropouts and urban. It was found that as percentage of dropouts increased, the crime rate per thousand inhabitants also increased. It is also evident from the data set that urban areas are having higher crime rates as compared to rural areas. Other variables included in the study did not have much impact on the dependent variable. The first column of the table below shows the regression coefficient of all the independent variables. The second column contains the standard error of the regression coefficient. The standard error of all the variables is the distance of the standard value from its true value. As can be seen from the table, the standard error of the constant term (intercept) is 1226 while it is 8.238 for the variable state. The third column is the calculated t-value for the data. This can be used to test that the corresponding population coefficient is 0. The probability that the population value for the corresponding variable is 0 is given in the last column. A row with small p-values and high t-values suggests that the variable is significant and contributes to the model. ...Download file to see next pagesRead More
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